Causas de Morte ao redor do mundo¶

Análise Exploratória¶

Importanto Base de dados e imprimindo as primeiras 5 linhas

In [2]:
import pandas as pd

# Importando base de dados 
df = pd.read_csv("./cause_of_deaths.csv")

# Primeiras 5 linhas do Dataframe
df.head()
Out[2]:
Country/Territory Code Year Meningitis Alzheimer's Disease and Other Dementias Parkinson's Disease Nutritional Deficiencies Malaria Drowning Interpersonal Violence ... Diabetes Mellitus Chronic Kidney Disease Poisonings Protein-Energy Malnutrition Road Injuries Chronic Respiratory Diseases Cirrhosis and Other Chronic Liver Diseases Digestive Diseases Fire, Heat, and Hot Substances Acute Hepatitis
0 Afghanistan AFG 1990 2159 1116 371 2087 93 1370 1538 ... 2108 3709 338 2054 4154 5945 2673 5005 323 2985
1 Afghanistan AFG 1991 2218 1136 374 2153 189 1391 2001 ... 2120 3724 351 2119 4472 6050 2728 5120 332 3092
2 Afghanistan AFG 1992 2475 1162 378 2441 239 1514 2299 ... 2153 3776 386 2404 5106 6223 2830 5335 360 3325
3 Afghanistan AFG 1993 2812 1187 384 2837 108 1687 2589 ... 2195 3862 425 2797 5681 6445 2943 5568 396 3601
4 Afghanistan AFG 1994 3027 1211 391 3081 211 1809 2849 ... 2231 3932 451 3038 6001 6664 3027 5739 420 3816

5 rows × 34 columns

Quantas entradas temos? de quantos anos? de quantos países? e de quantas doenças?

In [3]:
df.shape
Out[3]:
(6120, 34)
In [4]:
df['Year'].nunique()
Out[4]:
30
In [5]:
df['Country/Territory'].nunique()
Out[5]:
204
In [6]:
dfCauses = df.drop(columns=["Country/Territory", "Year"]).reset_index(drop=True)
dfCauses = dfCauses.melt(id_vars=["Code"], var_name="Cause", value_name="Deaths")
dfCauses["Cause"].nunique()
Out[6]:
31

Nota-se que há 6120 entradas, recorrentes a informações de 204 países ao longo de 30 anos a respeito de 31 causas de mortes distintas.

Abaixo estão listadas as causas de mortes que estamos trabalhando:

In [7]:
dfCauses["Cause"].unique()
Out[7]:
array(['Meningitis', "Alzheimer's Disease and Other Dementias",
       "Parkinson's Disease", 'Nutritional Deficiencies', 'Malaria',
       'Drowning', 'Interpersonal Violence', 'Maternal Disorders',
       'HIV/AIDS', 'Drug Use Disorders', 'Tuberculosis',
       'Cardiovascular Diseases', 'Lower Respiratory Infections',
       'Neonatal Disorders', 'Alcohol Use Disorders', 'Self-harm',
       'Exposure to Forces of Nature', 'Diarrheal Diseases',
       'Environmental Heat and Cold Exposure', 'Neoplasms',
       'Conflict and Terrorism', 'Diabetes Mellitus',
       'Chronic Kidney Disease', 'Poisonings',
       'Protein-Energy Malnutrition', 'Road Injuries',
       'Chronic Respiratory Diseases',
       'Cirrhosis and Other Chronic Liver Diseases', 'Digestive Diseases',
       'Fire, Heat, and Hot Substances', 'Acute Hepatitis'], dtype=object)

Abaixo está disposta a listagem dos anos. Nota-se que trabalharemos no período de tempo 1990 - 2019.

In [8]:
df['Year'].unique()
Out[8]:
array([1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,
       2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
       2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019], dtype=int64)

Há dados de todos os países para cada um dos anos?

In [9]:
# Número de entradas (uma por ano) para cada país

df['Country/Territory'].value_counts()
Out[9]:
Afghanistan         30
Papua New Guinea    30
Niue                30
North Korea         30
North Macedonia     30
                    ..
Greenland           30
Grenada             30
Guam                30
Guatemala           30
Zimbabwe            30
Name: Country/Territory, Length: 204, dtype: int64
In [10]:
# Verificar se há dados faltantes
df.isnull().sum()
Out[10]:
Country/Territory                             0
Code                                          0
Year                                          0
Meningitis                                    0
Alzheimer's Disease and Other Dementias       0
Parkinson's Disease                           0
Nutritional Deficiencies                      0
Malaria                                       0
Drowning                                      0
Interpersonal Violence                        0
Maternal Disorders                            0
HIV/AIDS                                      0
Drug Use Disorders                            0
Tuberculosis                                  0
Cardiovascular Diseases                       0
Lower Respiratory Infections                  0
Neonatal Disorders                            0
Alcohol Use Disorders                         0
Self-harm                                     0
Exposure to Forces of Nature                  0
Diarrheal Diseases                            0
Environmental Heat and Cold Exposure          0
Neoplasms                                     0
Conflict and Terrorism                        0
Diabetes Mellitus                             0
Chronic Kidney Disease                        0
Poisonings                                    0
Protein-Energy Malnutrition                   0
Road Injuries                                 0
Chronic Respiratory Diseases                  0
Cirrhosis and Other Chronic Liver Diseases    0
Digestive Diseases                            0
Fire, Heat, and Hot Substances                0
Acute Hepatitis                               0
dtype: int64

Observa-se que nossa base de dados está bem completa, sem dados faltantes.

Agora, fica a dúvida, qual a principal causa de morte do planeta?

In [11]:
# Novo dataset com a frequência de cada causa de morte no mundo
dfFrequence = df.groupby("Year").sum(numeric_only=True)
dfFrequence
Out[11]:
Meningitis Alzheimer's Disease and Other Dementias Parkinson's Disease Nutritional Deficiencies Malaria Drowning Interpersonal Violence Maternal Disorders HIV/AIDS Drug Use Disorders ... Diabetes Mellitus Chronic Kidney Disease Poisonings Protein-Energy Malnutrition Road Injuries Chronic Respiratory Diseases Cirrhosis and Other Chronic Liver Diseases Digestive Diseases Fire, Heat, and Hot Substances Acute Hepatitis
Year
1990 432253 560616 147156 756808 840297 460460 372497 302419 336059 56133 ... 661085 600925 87951 655975 1112770 3092759 1012423 1854392 123123 166343
1991 428621 583166 150875 729145 858984 454375 383689 298271 430725 61890 ... 679630 613589 87813 631013 1117024 3148288 1026870 1877515 123941 165276
1992 426440 605894 154886 700664 856415 447056 407176 299300 540070 66826 ... 702253 630160 88435 606015 1125566 3207816 1042953 1903759 124995 163687
1993 420836 629571 160249 674219 862216 445434 432858 293564 664463 71603 ... 728077 647255 90036 583919 1137444 3266612 1067730 1939556 127493 161899
1994 413799 652176 164381 649801 855671 443350 441971 293148 800169 76717 ... 751254 665365 90897 564046 1153642 3297292 1089331 1967669 129611 159423
1995 409826 674815 168882 723095 862626 437303 444246 290551 938440 79985 ... 773490 683701 90353 641084 1162799 3313295 1104380 1984263 128523 157173
1996 417259 696665 173822 671977 872476 423296 432673 287746 1061580 81704 ... 799023 704624 88861 593826 1162809 3342591 1115241 1995513 126804 153406
1997 400893 717342 179347 647682 892946 413405 427316 289467 1174154 82572 ... 827734 731048 87371 572372 1169798 3381872 1128962 2014659 126274 151902
1998 393364 738768 185097 620498 901338 407205 431984 289304 1303651 85087 ... 854415 758681 86679 549543 1177827 3401426 1141509 2030600 124746 149563
1999 390136 761620 191538 593417 893788 397169 440642 288836 1439777 87336 ... 879521 785380 87333 527431 1195303 3419612 1158552 2054201 125921 147017
2000 385994 786615 198545 568454 882055 387627 450393 287027 1559389 89690 ... 905965 814854 88799 505874 1212355 3450006 1177628 2079551 126659 144124
2001 379912 814526 206194 542565 912709 375917 451206 282455 1662064 87325 ... 934841 842790 89294 483074 1224220 3465831 1200250 2107791 126427 140143
2002 372298 845695 214930 517754 929195 361967 462331 276253 1746104 84750 ... 970544 874109 89920 462083 1237109 3490310 1227607 2143976 125532 134941
2003 369171 877011 222256 417264 959654 347745 446701 269425 1808445 82250 ... 1000276 902200 90634 365742 1248216 3491643 1251776 2173729 125730 130158
2004 363462 909148 227695 393952 960784 338417 440855 265279 1842954 81843 ... 1018138 922921 91455 345527 1258547 3462850 1266574 2187927 123651 127953
2005 361809 945619 236498 376740 940540 332705 435946 264407 1832074 84990 ... 1050925 956457 92064 329849 1269243 3479174 1293978 2226154 124166 128197
2006 358973 982308 242749 362476 941746 319875 427355 259261 1767868 85421 ... 1071057 981743 89859 316210 1267492 3461443 1300281 2235326 121265 124737
2007 352747 1022057 250475 347407 940193 311889 420025 252551 1669306 85962 ... 1092777 1010311 88475 302088 1273318 3467408 1311974 2251390 119600 120512
2008 343452 1065297 260382 335623 932910 304870 422800 247899 1559499 87077 ... 1122287 1045743 88687 290690 1284333 3510195 1330568 2280070 118269 117273
2009 336868 1109405 267738 322879 917796 296871 422750 244175 1453258 87025 ... 1142391 1074048 87902 279325 1281669 3498126 1330408 2279806 116177 112643
2010 326778 1155944 276188 322187 909814 290831 417449 239749 1364619 88079 ... 1165663 1107473 87632 279144 1278839 3509920 1340704 2298319 115329 109556
2011 312733 1201138 284488 314327 868564 281106 413880 234669 1283719 89322 ... 1197995 1143219 86099 271516 1267737 3539167 1348880 2316484 113655 106549
2012 300091 1247515 293123 298117 806183 275585 417694 227177 1198698 90934 ... 1236606 1176295 84595 255541 1255450 3558276 1354358 2329472 112090 104589
2013 289502 1294701 303488 291451 751776 266827 415620 223473 1126196 94562 ... 1277414 1214187 83618 249435 1232221 3610804 1359145 2348963 111818 98898
2014 280438 1343756 312928 284578 724324 258489 413371 214881 1072970 99295 ... 1320608 1247378 81976 242956 1213989 3651653 1363403 2364063 110102 92845
2015 267789 1394942 321782 278793 702890 254237 416362 208450 1029667 105401 ... 1366100 1286935 80974 237723 1201418 3690907 1384947 2404387 110167 88750
2016 261246 1451840 330736 273191 660364 248811 412500 203737 996098 112875 ... 1412896 1325004 79951 232544 1193556 3740794 1404134 2439649 109840 84726
2017 250132 1509646 339343 267463 629653 241842 417143 199197 947006 118324 ... 1454681 1351349 78484 227038 1188281 3795946 1425956 2478029 109165 82472
2018 241666 1568617 351322 258094 631568 240266 419249 198069 892341 123095 ... 1501633 1387668 77805 218222 1195919 3886637 1447651 2515213 110642 80894
2019 236084 1622426 362702 251411 643201 237069 414157 196306 863056 128048 ... 1549593 1426280 77130 212080 1197575 3972681 1471148 2556209 111199 79142

30 rows × 31 columns

In [12]:
# Causa de morte mais frequente no planeta em cada ano
dfMax = dfFrequence.idxmax(axis=1)
dfMax
Out[12]:
Year
1990    Cardiovascular Diseases
1991    Cardiovascular Diseases
1992    Cardiovascular Diseases
1993    Cardiovascular Diseases
1994    Cardiovascular Diseases
1995    Cardiovascular Diseases
1996    Cardiovascular Diseases
1997    Cardiovascular Diseases
1998    Cardiovascular Diseases
1999    Cardiovascular Diseases
2000    Cardiovascular Diseases
2001    Cardiovascular Diseases
2002    Cardiovascular Diseases
2003    Cardiovascular Diseases
2004    Cardiovascular Diseases
2005    Cardiovascular Diseases
2006    Cardiovascular Diseases
2007    Cardiovascular Diseases
2008    Cardiovascular Diseases
2009    Cardiovascular Diseases
2010    Cardiovascular Diseases
2011    Cardiovascular Diseases
2012    Cardiovascular Diseases
2013    Cardiovascular Diseases
2014    Cardiovascular Diseases
2015    Cardiovascular Diseases
2016    Cardiovascular Diseases
2017    Cardiovascular Diseases
2018    Cardiovascular Diseases
2019    Cardiovascular Diseases
dtype: object
In [13]:
mode = dfMax.mode()
cases = dfFrequence["Cardiovascular Diseases"].sum(axis=0)
print("A maior causa de morte no planeta de 1990 a 2019:", mode[0], " - tirou a vida de", cases, "individuos.")

A maior causa de morte no planeta de 1990 a 2019: Cardiovascular Diseases  - tirou a vida de 447741982 individuos.
In [14]:
# Outra análise interessante: Qual a causa de morte mais frequente em cada país?
# Para isso, vamos criar um novo dataset com a frequência de cada causa de morte em cada país, em todos os anos

dfCountry = df.drop(columns=["Year"]).reset_index(drop=True)
dfCountry = dfCountry.groupby("Country/Territory").sum(numeric_only=True)
dfCountry
Out[14]:
Meningitis Alzheimer's Disease and Other Dementias Parkinson's Disease Nutritional Deficiencies Malaria Drowning Interpersonal Violence Maternal Disorders HIV/AIDS Drug Use Disorders ... Diabetes Mellitus Chronic Kidney Disease Poisonings Protein-Energy Malnutrition Road Injuries Chronic Respiratory Diseases Cirrhosis and Other Chronic Liver Diseases Digestive Diseases Fire, Heat, and Hot Substances Acute Hepatitis
Country/Territory
Afghanistan 78666 41998 13397 71453 13924 56536 108228 129621 4282 7094 ... 93207 134676 14530 70163 208331 209857 98419 186959 13559 98108
Albania 1323 16549 4491 569 0 2397 5242 246 57 634 ... 4055 7636 500 526 8522 22632 8717 14907 636 44
Algeria 15685 86914 22943 7138 70 24273 16702 29475 6101 10612 ... 89035 154666 12337 6407 369395 168453 91927 146527 27628 10492
American Samoa 30 143 69 60 0 120 101 30 15 0 ... 970 512 0 60 164 612 181 341 0 0
Andorra 0 614 137 0 0 0 15 0 85 0 ... 198 292 0 0 259 838 283 560 0 30
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Venezuela 11615 108735 18573 22554 3726 20273 266071 12739 46090 1664 ... 175790 161667 2607 21347 175036 122198 91720 168365 4949 1109
Vietnam 38559 369363 83322 48613 17311 214356 47981 13167 148838 19959 ... 544222 396874 34681 7366 594980 911787 527192 735817 17380 30650
Yemen 21095 31045 7188 68939 143463 27994 17918 53611 6276 3718 ... 30812 52119 12561 66731 278327 126525 64136 111536 23871 26532
Zambia 98886 13473 4054 95913 205529 12809 30065 28395 1175563 933 ... 54098 41751 9056 92915 56976 59173 100581 147640 9476 8846
Zimbabwe 41238 20017 5764 66723 118728 18169 32741 29802 1836042 2271 ... 71175 49952 9113 65942 67207 71774 55027 108691 14718 3778

204 rows × 31 columns

In [15]:
# Qual será o país que mais teve mortes por cada causa de morte?
dfLeaders = dfCountry.idxmax(axis=0)
dfLeaders
Out[15]:
Meningitis                                            India
Alzheimer's Disease and Other Dementias               China
Parkinson's Disease                                   China
Nutritional Deficiencies                              India
Malaria                                             Nigeria
Drowning                                              China
Interpersonal Violence                               Brazil
Maternal Disorders                                    India
HIV/AIDS                                       South Africa
Drug Use Disorders                            United States
Tuberculosis                                          India
Cardiovascular Diseases                               China
Lower Respiratory Infections                          India
Neonatal Disorders                                    India
Alcohol Use Disorders                                Russia
Self-harm                                             India
Exposure to Forces of Nature                          Haiti
Diarrheal Diseases                                    India
Environmental Heat and Cold Exposure                 Russia
Neoplasms                                             China
Conflict and Terrorism                               Rwanda
Diabetes Mellitus                                     India
Chronic Kidney Disease                                India
Poisonings                                            China
Protein-Energy Malnutrition                           India
Road Injuries                                         China
Chronic Respiratory Diseases                          China
Cirrhosis and Other Chronic Liver Diseases            India
Digestive Diseases                                    India
Fire, Heat, and Hot Substances                        India
Acute Hepatitis                                       India
dtype: object
In [16]:
dfCountry.idxmax(axis=0).mode()[0]
Out[16]:
'India'

Dado a população numerosa da índia, faz sentido que ela lidere em número de óbitos para a maior parte das causas. Será que o Brasil lidera em alguma causa? Vejamos:

In [17]:
# Em quais causas de morte o Brasil aparece no dataset dfLeaders?
dfLeaders[dfLeaders == "Brazil"]
Out[17]:
Interpersonal Violence    Brazil
dtype: object

O Brasil é o país onde houve mais mortes por Violência Interpessoal nos anos de 1990 a 2019.

Vamos agora construir algumas visualizações.

Visualização¶

Vamos começar analisando a causa de morte que o Brasil Lidera: a Interpesonal Violence.

In [18]:
import plotly.express as px

# Gráfico de linhas para mostrar a evolução do número de mortes por INTERPERSONAL VIOLENCE no Brasil

fig = px.line(df, x="Year", y="Interpersonal Violence", color="Country/Territory", title="Interpersonal Violence")
fig.show()

Faremos, agora, um gráfico de barras que mostra o número de mortes por cada causa no Brasil em 2019 - a data mais recente.

In [21]:
# Gráfico de barras com o número de mortes em cada país por cada causa em 2019

df2019 = df[df["Year"] == 2019]
df2019 = df2019.loc[df["Country/Territory"] == "Brazil"].drop(columns=["Country/Territory","Code", "Year"]).reset_index(drop=True)

# Agora usamos melt para transformar as colunas em linhas
df2019= df2019.melt(var_name="Cause", value_name="Deaths")
bar = px.bar(df2019,
            x="Deaths",
            y="Cause",
            color="Cause",
        color_discrete_sequence=["yellow", "blue", "green"],
)
# Inserir título
bar.update_layout(title="Deaths in Brazil in 2019")

# Não mostrar a legenda
bar.update_layout(showlegend=False)

bar

E no mundo? Como se distribuiram as causas em 2019?

In [20]:
# Gráfico de barras com o número total de mortes no planeta por cada causa de morte em 2019
bar2 = px.bar(dfFrequence.loc[2019],
            x=dfFrequence.loc[2019].values,
            y=dfFrequence.loc[2019].index,
            color=dfFrequence.loc[2019].index,
            color_discrete_sequence=px.colors.sequential.RdBu,
)
# Inserir título
bar2.update_layout(title="Deaths in the world in 2019")

# Não mostrar a legenda
bar2.update_layout(showlegend=False)

# Não mostrar títulos dos eixos
bar2.update_xaxes(title_text="")
bar2.update_yaxes(title_text="")

bar2

Essas foram algumas das centenas de visualizações interessantes que essa base de dados nos permite fazer.